Fast Adaptation to Super-Resolution Networks via Meta-learningopen access
- Authors
- Park, Seobin; Yoo, Jinsu; Cho, Donghyeon; Kim, Jiwon; Kim, Tae Hyun
- Issue Date
- Aug-2020
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Keywords
- Deep learning; Meta-learning; Patch recurrence; Single-image super-resolution
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.12372 LNCS, pp.754 - 769
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume
- 12372 LNCS
- Start Page
- 754
- End Page
- 769
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145341
- DOI
- 10.1007/978-3-030-58583-9_45
- ISSN
- 0302-9743
- Abstract
- Conventional supervised super-resolution (SR) approaches are trained with massive external SR datasets but fail to exploit desirable properties of the given test image. On the other hand, self-supervised SR approaches utilize the internal information within a test image but suffer from computational complexity in run-time. In this work, we observe the opportunity for further improvement of the performance of single-image super-resolution (SISR) without changing the architecture of conventional SR networks by practically exploiting additional information given from the input image. In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time. Then, in the test stage, parameters of this meta-learned network are rapidly fine-tuned with only a few iterations by only using the given low-resolution image. The adaptation at the test time takes full advantage of patch-recurrence property observed in natural images. Our method effectively handles unknown SR kernels and can be applied to any existing model. We demonstrate that the proposed model-agnostic approach consistently improves the performance of conventional SR networks on various benchmark SR datasets.
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